README.md

---
license: cc0-1.0
task_categories:
  - text-generation
  - feature-extraction
language:
  - en
tags:
  - corpus
  - leadership
  - historical
  - deku-corpus-builder
size_categories:
  - 1K<n<10K
---

# Time Management

This corpus was automatically generated by the **Deku Corpus Builder** for use in RAG-based AI applications.

## Dataset Description

- **Subject**: Time management
- **Subject Type**: topic
- **Total Items**: 349
- **Has Embeddings**: Yes (all-MiniLM-L6-v2)
- **Created**: 2026-01-03

## Dataset Structure

Each record contains:
- `text`: The content text
- `source_url`: Original source URL
- `source_title`: Title of the source document
- `source_domain`: Domain of the source
- `relevance_score`: Relevance to the subject (0-1)
- `quality_score`: Content quality score (0-1)
- `topics`: JSON array of detected topics
- `character_count`: Length of the text
- `subject_name`: The subject this content relates to
- `subject_type`: "personality" or "topic"
- `extraction_date`: When the content was extracted
- `embedding`: Pre-computed 384-dimensional embedding vector

## Usage

```python
from datasets import load_dataset

dataset = load_dataset("PhillyMac/Time_management_Corpus")

# Access the data
for item in dataset["train"]:
    print(item["text"][:100])
```

## Integration with RAG

This dataset is designed to be integrated with existing embedded corpuses. The embeddings use the `sentence-transformers/all-MiniLM-L6-v2` model, compatible with FAISS indexing.

## License

Content is sourced from public domain and Creative Commons licensed materials.

## Generated By

[Deku Corpus Builder](https://github.com/PhillyMac/deku-corpus-builder) - An automated corpus building system for AI applications.